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The Metric That Will Predict Your Company's Fortunes

On the face of it, Starbucks hit a home run when it released its fourth quarter earnings in early November. The coffee shop chain had strong earnings on the back of $21 billion of revenues for the full fiscal year. And it continued to perform exceptionally well on mobile payment systems — one quarter of its U.S. transactions are paid through its smartphone apps.

Still, as hugely impressive as these results are, Starbucks has a lot to learn about acquiring and keeping online customers.

I know this because I used to frequent Starbucks for my daily cup of Joe, but I stopped after deciding that going decaffeinated would eliminate my morning jitters. I read about the techniques employed by various roasters. Here’s what I learned: A century ago, coffee was decaffeinated using the chemical benzene, a cancer-causing agent. These days, many coffee processors use methyl chloride. After much research and a great deal of taste testing, I decided to only drink organic coffee decaffeinated using the Swiss Water filtered process, that only uses “local water and a dash of loving attention” to remove 99.9% of the caffeine.

My point is, that while Starbucks is schooling most American businesses in embracing mobile technology, they are dropping the ball on something vital to the long-term success of most firms — learning their customers’ trigger points for retention and increased purchases. For example, while I went to Starbucks frequently and used their excellent payment app, the company never asked why I stopped. Had they asked me, they would have learned about my conversion to the Swiss Water method. Starbucks could then have tested to learn if others shared my concerns and reacted accordingly.

Businesses must figure out these customer trigger points. In my case, it’s a desire for a certain coffee product. Without that knowledge, Starbucks cannot win me back.

Over the past 15 years there have been great advances in science-based techniques to optimize customer acquisition and retention, although many companies use these as blunt tools rather than the refined instruments they can be. With the rising costs of using such media as Google and Facebook to acquire customers, it’s vital that companies become more scientific in getting and keeping customers. The reason Facebook’s earnings are so strong is that they have learned to effectively target customers, allowing them to charge more and more per ad impression.

But with all the science and tools behind optimizing traffic buys and conversion, most companies have yet to tap in the rich opportunities to simply make more money and drive higher customer satisfaction with the customers they already have acquired.

To lift performance with its current customers, there’s one crucial data point a company has to start to measure — Customer Lifetime Value.

In a nutshell, CLV is a number that tells a company what each customer is really worth. It’s the total value of a customer based on their revenue and variable costs, data typically drawn from customer relationship management systems.

Calculating CLV starts with tracking all orders from a customer and the associated expenses of acquiring and keeping that client. Costs should not include fixed business costs but should capture all variable costs—marketing and acquisition budgets, product and shipping costs, maintenance and transaction fees, refunds, chargebacks, and the cost of customer service inquires. Once logged in a spreadsheet, that data allows a business know which customers are most valuable.

If a customer had a CLV of $100 and an acquisition cost of $85, you might assume the customer’s return on investment was $100/$85, or 117 percent. However, by calculating the present value (PV) of a customer’s CLV—discounting the value of future orders to its value today—a more accurate ROI can be calculated. Assuming a 10 percent discount factor, the PV of a $100 CLV would be $92.87 and the ROI would be 109 percent ($92.87/$85.)

Customer Lifetime Value can be tracked dynamically if the model is connected to live data from a CRM. It can even be used to look into the future, calculating Estimated Customer Lifetime Value. Companies that don’t know this number, what drives its performance and how to use it to lift satisfaction and mine additional revenues from their customers are destined to fail.

Using scientific A/B testing of ongoing customers can optimize CLV, generating predictive models suggesting how that number can change based on different campaigns. Applied on a dynamic platform that updates constantly, it’s an approach that would tell Starbucks that I have stopped buying their coffee by showing my Estimated Customer Lifetime Value drop to zero.

In fact, Starbucks could have used that data to put me into a cohort, perhaps one of customers who skew more to health concerns about food products. Then, they could continue to nurture me with information about healthy drink and food options they have, how their food products are sourced and special offers to get me to try healthy menu items. Done over a large sample set of customers this could build retention and lift average revenue per customer all the while making their customers happier about their ongoing Starbucks experience.

Whether it’s a company like Netflix trying to figure out what makes their customers tune in or a firm like Twitter pondering how to monetize a massive user base, using such scientific methods can only help. Done right, it might even get me back to Starbucks for my morning coffee.